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test_beam.py
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test_beam.py
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import os
import tempfile
from unittest import TestCase
import apache_beam as beam
import nlp
class DummyBeamDataset(nlp.BeamBasedBuilder):
"""Dummy beam dataset."""
def _info(self):
return nlp.DatasetInfo(
features=nlp.Features({"content": nlp.Value("string")}),
# No default supervised_keys.
supervised_keys=None,
)
def _split_generators(self, dl_manager, pipeline):
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"examples": get_test_dummy_examples()})]
def _build_pcollection(self, pipeline, examples):
return pipeline | "Load Examples" >> beam.Create(examples)
class NestedBeamDataset(nlp.BeamBasedBuilder):
"""Dummy beam dataset."""
def _info(self):
return nlp.DatasetInfo(
features=nlp.Features({"a": nlp.Sequence({"b": nlp.Value("string")})}),
# No default supervised_keys.
supervised_keys=None,
)
def _split_generators(self, dl_manager, pipeline):
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"examples": get_test_nested_examples()})]
def _build_pcollection(self, pipeline, examples):
return pipeline | "Load Examples" >> beam.Create(examples)
def get_test_dummy_examples():
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]
def get_test_nested_examples():
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])] * 10_000
class BeamBuilderTest(TestCase):
def test_download_and_prepare(self):
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
tmp_cache_dir, "dummy_beam_dataset", "default", "0.0.0", "dummy_beam_dataset-train.arrow"
)
)
)
self.assertDictEqual(builder.info.features, nlp.Features({"content": nlp.Value("string")}))
dset = builder.as_dataset()
self.assertEqual(dset["train"].num_rows, expected_num_examples)
self.assertEqual(dset["train"].info.splits["train"].num_examples, expected_num_examples)
self.assertDictEqual(dset["train"][0], get_test_dummy_examples()[0][1])
self.assertDictEqual(
dset["train"][expected_num_examples - 1], get_test_dummy_examples()[expected_num_examples - 1][1]
)
self.assertTrue(
os.path.exists(
os.path.join(tmp_cache_dir, "dummy_beam_dataset", "default", "0.0.0", "dataset_info.json")
)
)
def test_no_beam_options(self):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir)
self.assertRaises(nlp.builder.MissingBeamOptions, builder.download_and_prepare)
def test_nested_features(self):
expected_num_examples = len(get_test_nested_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = NestedBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
tmp_cache_dir, "nested_beam_dataset", "default", "0.0.0", "nested_beam_dataset-train.arrow"
)
)
)
self.assertDictEqual(builder.info.features, nlp.Features({"a": nlp.Sequence({"b": nlp.Value("string")})}))
dset = builder.as_dataset()
self.assertEqual(dset["train"].num_rows, expected_num_examples)
self.assertEqual(dset["train"].info.splits["train"].num_examples, expected_num_examples)
self.assertDictEqual(dset["train"][0], get_test_nested_examples()[0][1])
self.assertDictEqual(
dset["train"][expected_num_examples - 1], get_test_nested_examples()[expected_num_examples - 1][1]
)
self.assertTrue(
os.path.exists(
os.path.join(tmp_cache_dir, "nested_beam_dataset", "default", "0.0.0", "dataset_info.json")
)
)